Bokeh is an interactive visualization library for modern web browsers. It provides elegant, concise construction of versatile graphics, and affords high-performance interactivity over large or streaming datasets. Bokeh can help anyone who would like to quickly and easily make interactive plots, dashboards, and data applications. Source: https://docs.bokeh.org/en/latest/
Bokeh Templates
# Importando o módulo Bokeh
import bokeh
from bokeh.io import show, output_notebook
from bokeh.plotting import figure, output_file
from bokeh.models import ColumnDataSource
from bokeh.transform import factor_cmap
from bokeh.palettes import Spectral6
# Carregando o Bokeh
output_notebook()
# Arquivo gerado pela visualização
output_file("Bokeh-Grafico-Interativo.html")
p = figure()
p.line([1, 2, 3, 4, 5], [6, 7, 2, 4, 5], line_width = 2)
show(p)
# Criando um novo gráfico
output_file("Bokeh-Grafico-Barras.html")
fruits = ['Maças', 'Peras', 'Tangerinas', 'Uvas', 'Melancias', 'Morangos']
counts = [5, 3, 4, 2, 4, 6]
source = ColumnDataSource(data=dict(fruits=fruits, counts=counts))
p = figure(x_range=fruits, plot_height=350, toolbar_location=None, title="Contagem de Frutas")
p.vbar(x='fruits',
top='counts',
width=0.9,
source=source,
legend_label="fruits",
line_color='white',
fill_color=factor_cmap('fruits', palette=Spectral6, factors=fruits))
p.xgrid.grid_line_color = None
p.y_range.start = 0
p.y_range.end = 9
p.legend.orientation = "horizontal"
p.legend.location = "top_center"
show(p)
from bokeh.io import output_file, show
from bokeh.models import FactorRange
from bokeh.plotting import figure
output_file("bar_mixed.html")
factors = [
("Q1", "jan"), ("Q1", "feb"), ("Q1", "mar"),
("Q2", "apr"), ("Q2", "may"), ("Q2", "jun"),
("Q3", "jul"), ("Q3", "aug"), ("Q3", "sep"),
("Q4", "oct"), ("Q4", "nov"), ("Q4", "dec"),
]
p = figure(x_range=FactorRange(*factors), plot_height=350,
toolbar_location=None, tools="")
x = [ 10, 12, 16, 9, 10, 8, 12, 13, 14, 14, 12, 16 ]
p.vbar(x=factors, top=x, width=0.9, alpha=0.5)
p.line(x=["Q1", "Q2", "Q3", "Q4"], y=[12, 9, 13, 14], color="red", line_width=2)
p.y_range.start = 0
p.x_range.range_padding = 0.1
p.xaxis.major_label_orientation = 1
p.xgrid.grid_line_color = None
show(p)
# Construindo um ScatterPlot
from bokeh.plotting import figure, show, output_file
from bokeh.sampledata.iris import flowers
colormap = {'setosa': 'red', 'versicolor': 'green', 'virginica': 'blue'}
colors = [colormap[x] for x in flowers['species']]
p = figure(title = "Iris Morphology")
p.xaxis.axis_label = 'Petal Length'
p.yaxis.axis_label = 'Petal Width'
p.circle(flowers["petal_length"], flowers["petal_width"], color=colors, fill_alpha=0.2, size=10)
output_file("Bokeh_grafico_Iris.html", title="iris.py example")
show(p)
from bokeh.plotting import figure, output_file, show
# Outuput
output_file("Bokeh-Grafico-Circulos.html")
p = figure(plot_width = 400, plot_height = 400)
# Adicionando cÃrculos ao gráfico
p.circle([1, 2, 3, 4, 5], [6, 7, 2, 4, 5], size = 20, color = "navy", alpha = 0.5)
# Mostrando o resultado
show(p)
# Geojson
from bokeh.io import output_file, show
from bokeh.models import GeoJSONDataSource
from bokeh.plotting import figure
from bokeh.sampledata.sample_geojson import geojson
geo_source = GeoJSONDataSource(geojson=geojson)
p = figure()
p.circle(x = 'x', y = 'y', alpha = 0.9, source = geo_source)
output_file("Bokeh-GeoJSON.html")
show(p)
# Baixando o diretório de dados de exemplo do Bokeh
bokeh.sampledata.download()
from bokeh.io import show
from bokeh.models import (ColumnDataSource, HoverTool, LogColorMapper)
from bokeh.palettes import Viridis6 as palette
from bokeh.plotting import figure
from bokeh.sampledata.us_counties import data as counties
from bokeh.sampledata.unemployment import data as unemployment
palette.reverse()
counties = {code: county for code, county in counties.items() if county["state"] == "tx"}
county_xs = [county["lons"] for county in counties.values()]
county_ys = [county["lats"] for county in counties.values()]
county_names = [county['name'] for county in counties.values()]
county_rates = [unemployment[county_id] for county_id in counties]
color_mapper = LogColorMapper(palette=palette)
source = ColumnDataSource(data=dict(
x=county_xs,
y=county_ys,
name=county_names,
rate=county_rates,
))
TOOLS = "pan,wheel_zoom,reset,hover,save"
p = figure(
title="Texas Unemployment, 2009", tools=TOOLS,
x_axis_location=None, y_axis_location=None
)
p.grid.grid_line_color = None
p.patches('x', 'y', source=source,
fill_color={'field': 'rate', 'transform': color_mapper},
fill_alpha=0.7, line_color="white", line_width=0.5)
hover = p.select_one(HoverTool)
hover.point_policy = "follow_mouse"
hover.tooltips = [
("Name", "@name"),
("Unemployment rate)", "@rate%"),
("(Long, Lat)", "($x, $y)"),
]
show(p)
from bokeh.plotting import figure, output_file, show
from bokeh.sampledata.iris import flowers
colormap = {'setosa': 'red', 'versicolor': 'green', 'virginica': 'blue'}
colors = [colormap[x] for x in flowers['species']]
p = figure(title = "Iris Morphology")
p.xaxis.axis_label = 'Petal Length'
p.yaxis.axis_label = 'Petal Width'
p.circle(flowers["petal_length"], flowers["petal_width"],
color=colors, fill_alpha=0.2, size=10)
output_file("iris.html", title="iris.py example")
show(p)
import numpy as np
from bokeh.plotting import figure, output_file, show
N = 20
img = np.empty((N,N), dtype=np.uint32)
view = img.view(dtype=np.uint8).reshape((N, N, 4))
for i in range(N):
for j in range(N):
view[i, j, 0] = int(i/N*255)
view[i, j, 1] = 158
view[i, j, 2] = int(j/N*255)
view[i, j, 3] = 255
p = figure(tooltips=[("x", "$x"), ("y", "$y"), ("value", "@image")])
p.x_range.range_padding = p.y_range.range_padding = 0
# must give a vector of images
p.image_rgba(image=[img], x=0, y=0, dw=10, dh=10)
output_file("image_rgba.html", title="image_rgba.py example")
show(p) # open a browser
import numpy as np
import pandas as pd
from bokeh.palettes import brewer
from bokeh.plotting import figure, output_file, show
output_file('stacked_area.html')
N = 10
df = pd.DataFrame(np.random.randint(10, 100, size=(15, N))).add_prefix('y')
p = figure(x_range=(0, len(df)-1), y_range=(0, 800))
p.grid.minor_grid_line_color = '#eeeeee'
names = ["y%d" % i for i in range(N)]
p.varea_stack(stackers=names, x='index', color=brewer['Spectral'][N], legend_label=names, source=df)
# reverse the legend entries to match the stacked order
p.legend.items.reverse()
show(p)
import numpy as np
from bokeh.plotting import figure, output_file, show
N = 500
x = np.linspace(0, 10, N)
y = np.linspace(0, 10, N)
xx, yy = np.meshgrid(x, y)
d = np.sin(xx)*np.cos(yy)
p = figure(tooltips=[("x", "$x"), ("y", "$y"), ("value", "@image")])
p.x_range.range_padding = p.y_range.range_padding = 0
# must give a vector of image data for image parameter
p.image(image=[d], x=0, y=0, dw=10, dh=10, palette="Spectral11", level="image")
p.grid.grid_line_width = 0.5
output_file("image.html", title="image.py example")
show(p)
import numpy as np
from bokeh.layouts import gridplot
from bokeh.plotting import figure, output_file, show
from bokeh.sampledata.stocks import AAPL, GOOG, IBM, MSFT
def datetime(x):
return np.array(x, dtype=np.datetime64)
p1 = figure(x_axis_type="datetime", title="Stock Closing Prices")
p1.grid.grid_line_alpha=0.3
p1.xaxis.axis_label = 'Date'
p1.yaxis.axis_label = 'Price'
p1.line(datetime(AAPL['date']), AAPL['adj_close'], color='#A6CEE3', legend_label='AAPL')
p1.line(datetime(GOOG['date']), GOOG['adj_close'], color='#B2DF8A', legend_label='GOOG')
p1.line(datetime(IBM['date']), IBM['adj_close'], color='#33A02C', legend_label='IBM')
p1.line(datetime(MSFT['date']), MSFT['adj_close'], color='#FB9A99', legend_label='MSFT')
p1.legend.location = "top_left"
aapl = np.array(AAPL['adj_close'])
aapl_dates = np.array(AAPL['date'], dtype=np.datetime64)
window_size = 30
window = np.ones(window_size)/float(window_size)
aapl_avg = np.convolve(aapl, window, 'same')
p2 = figure(x_axis_type="datetime", title="AAPL One-Month Average")
p2.grid.grid_line_alpha = 0
p2.xaxis.axis_label = 'Date'
p2.yaxis.axis_label = 'Price'
p2.ygrid.band_fill_color = "olive"
p2.ygrid.band_fill_alpha = 0.1
p2.circle(aapl_dates, aapl, size=4, legend_label='close',
color='darkgrey', alpha=0.2)
p2.line(aapl_dates, aapl_avg, legend_label='avg', color='navy')
p2.legend.location = "top_left"
output_file("stocks.html", title="stocks.py example")
show(gridplot([[p1,p2]], plot_width=400, plot_height=400)) # open a browser
import numpy as np
from bokeh.layouts import gridplot
from bokeh.plotting import figure, output_file, show
x = np.linspace(0, 4*np.pi, 100)
y = np.sin(x)
TOOLS = "pan,wheel_zoom,box_zoom,reset,save,box_select"
p1 = figure(title="Legend Example", tools=TOOLS)
p1.circle(x, y, legend_label="sin(x)")
p1.circle(x, 2*y, legend_label="2*sin(x)", color="orange")
p1.circle(x, 3*y, legend_label="3*sin(x)", color="green")
p1.legend.title = 'Example Title'
p2 = figure(title="Another Legend Example", tools=TOOLS)
p2.circle(x, y, legend_label="sin(x)")
p2.line(x, y, legend_label="sin(x)")
p2.line(x, 2*y, legend_label="2*sin(x)", line_dash=(4, 4), line_color="orange", line_width=2)
p2.square(x, 3*y, legend_label="3*sin(x)", fill_color=None, line_color="green")
p2.line(x, 3*y, legend_label="3*sin(x)", line_color="green")
output_file("legend.html", title="legend.py example")
show(gridplot([p1, p2], ncols=2, plot_width=400, plot_height=400)) # open a browser
import numpy as np
from scipy.integrate import odeint
from bokeh.plotting import figure, output_file, show
sigma = 10
rho = 28
beta = 8.0/3
theta = 3 * np.pi / 4
def lorenz(xyz, t):
x, y, z = xyz
x_dot = sigma * (y - x)
y_dot = x * rho - x * z - y
z_dot = x * y - beta* z
return [x_dot, y_dot, z_dot]
initial = (-10, -7, 35)
t = np.arange(0, 100, 0.006)
solution = odeint(lorenz, initial, t)
x = solution[:, 0]
y = solution[:, 1]
z = solution[:, 2]
xprime = np.cos(theta) * x - np.sin(theta) * y
colors = ["#C6DBEF", "#9ECAE1", "#6BAED6", "#4292C6", "#2171B5", "#08519C", "#08306B",]
p = figure(title="Lorenz attractor example", background_fill_color="#fafafa")
p.multi_line(np.array_split(xprime, 7), np.array_split(z, 7),
line_color=colors, line_alpha=0.8, line_width=1.5)
output_file("lorenz.html", title="lorenz.py example")
show(p)
from math import pi
import pandas as pd
from bokeh.plotting import figure, output_file, show
from bokeh.sampledata.stocks import MSFT
df = pd.DataFrame(MSFT)[:50]
df["date"] = pd.to_datetime(df["date"])
inc = df.close > df.open
dec = df.open > df.close
w = 12*60*60*1000 # half day in ms
TOOLS = "pan,wheel_zoom,box_zoom,reset,save"
p = figure(x_axis_type="datetime", tools=TOOLS, plot_width=1000, title = "MSFT Candlestick")
p.xaxis.major_label_orientation = pi/4
p.grid.grid_line_alpha=0.3
p.segment(df.date, df.high, df.date, df.low, color="black")
p.vbar(df.date[inc], w, df.open[inc], df.close[inc], fill_color="#D5E1DD", line_color="black")
p.vbar(df.date[dec], w, df.open[dec], df.close[dec], fill_color="#F2583E", line_color="black")
output_file("candlestick.html", title="candlestick.py example")
show(p) # open a browser
import numpy as np
from bokeh.plotting import figure, output_file, show
N = 4000
x = np.random.random(size=N) * 100
y = np.random.random(size=N) * 100
radii = np.random.random(size=N) * 1.5
colors = [
"#%02x%02x%02x" % (int(r), int(g), 150) for r, g in zip(50+2*x, 30+2*y)
]
TOOLS="hover,crosshair,pan,wheel_zoom,zoom_in,zoom_out,box_zoom,undo,redo,reset,tap,save,box_select,poly_select,lasso_select,"
p = figure(tools=TOOLS)
p.scatter(x, y, radius=radii,
fill_color=colors, fill_alpha=0.6,
line_color=None)
output_file("color_scatter.html", title="color_scatter.py example")
show(p) # open a browser
import numpy as np
import scipy.special
from bokeh.layouts import gridplot
from bokeh.plotting import figure, output_file, show
def make_plot(title, hist, edges, x, pdf, cdf):
p = figure(title=title, tools='', background_fill_color="#fafafa")
p.quad(top=hist, bottom=0, left=edges[:-1], right=edges[1:],
fill_color="navy", line_color="white", alpha=0.5)
p.line(x, pdf, line_color="#ff8888", line_width=4, alpha=0.7, legend_label="PDF")
p.line(x, cdf, line_color="orange", line_width=2, alpha=0.7, legend_label="CDF")
p.y_range.start = 0
p.legend.location = "center_right"
p.legend.background_fill_color = "#fefefe"
p.xaxis.axis_label = 'x'
p.yaxis.axis_label = 'Pr(x)'
p.grid.grid_line_color="white"
return p
#1
# Normal Distribution
mu, sigma = 0, 0.5
measured = np.random.normal(mu, sigma, 1000)
hist, edges = np.histogram(measured, density=True, bins=50)
x = np.linspace(-2, 2, 1000)
pdf = 1/(sigma * np.sqrt(2*np.pi)) * np.exp(-(x-mu)**2 / (2*sigma**2))
cdf = (1+scipy.special.erf((x-mu)/np.sqrt(2*sigma**2)))/2
p1 = make_plot("Normal Distribution (μ=0, σ=0.5)", hist, edges, x, pdf, cdf)
#2
# Log-Normal Distribution
mu, sigma = 0, 0.5
measured = np.random.lognormal(mu, sigma, 1000)
hist, edges = np.histogram(measured, density=True, bins=50)
x = np.linspace(0.0001, 8.0, 1000)
pdf = 1/(x* sigma * np.sqrt(2*np.pi)) * np.exp(-(np.log(x)-mu)**2 / (2*sigma**2))
cdf = (1+scipy.special.erf((np.log(x)-mu)/(np.sqrt(2)*sigma)))/2
p2 = make_plot("Log Normal Distribution (μ=0, σ=0.5)", hist, edges, x, pdf, cdf)
#3
# Gamma Distribution
k, theta = 7.5, 1.0
measured = np.random.gamma(k, theta, 1000)
hist, edges = np.histogram(measured, density=True, bins=50)
x = np.linspace(0.0001, 20.0, 1000)
pdf = x**(k-1) * np.exp(-x/theta) / (theta**k * scipy.special.gamma(k))
cdf = scipy.special.gammainc(k, x/theta)
p3 = make_plot("Gamma Distribution (k=7.5, θ=1)", hist, edges, x, pdf, cdf)
#4
# Weibull Distribution
lam, k = 1, 1.25
measured = lam*(-np.log(np.random.uniform(0, 1, 1000)))**(1/k)
hist, edges = np.histogram(measured, density=True, bins=50)
x = np.linspace(0.0001, 8, 1000)
pdf = (k/lam)*(x/lam)**(k-1) * np.exp(-(x/lam)**k)
cdf = 1 - np.exp(-(x/lam)**k)
p4 = make_plot("Weibull Distribution (λ=1, k=1.25)", hist, edges, x, pdf, cdf)
output_file('histogram.html', title="histogram.py example")
show(gridplot([p1,p2,p3,p4], ncols=2, plot_width=400, plot_height=400, toolbar_location=None))
from bokeh.io import output_file, show
from bokeh.plotting import figure
from bokeh.sampledata.periodic_table import elements
from bokeh.transform import dodge, factor_cmap
output_file("periodic.html")
periods = ["I", "II", "III", "IV", "V", "VI", "VII"]
groups = [str(x) for x in range(1, 19)]
df = elements.copy()
df["atomic mass"] = df["atomic mass"].astype(str)
df["group"] = df["group"].astype(str)
df["period"] = [periods[x-1] for x in df.period]
df = df[df.group != "-"]
df = df[df.symbol != "Lr"]
df = df[df.symbol != "Lu"]
cmap = {
"alkali metal" : "#a6cee3",
"alkaline earth metal" : "#1f78b4",
"metal" : "#d93b43",
"halogen" : "#999d9a",
"metalloid" : "#e08d49",
"noble gas" : "#eaeaea",
"nonmetal" : "#f1d4Af",
"transition metal" : "#599d7A",
}
TOOLTIPS = [
("Name", "@name"),
("Atomic number", "@{atomic number}"),
("Atomic mass", "@{atomic mass}"),
("Type", "@metal"),
("CPK color", "$color[hex, swatch]:CPK"),
("Electronic configuration", "@{electronic configuration}"),
]
p = figure(title="Periodic Table (omitting LA and AC Series)", plot_width=1000, plot_height=450,
x_range=groups, y_range=list(reversed(periods)),
tools="hover", toolbar_location=None, tooltips=TOOLTIPS)
r = p.rect("group", "period", 0.95, 0.95, source=df, fill_alpha=0.6, legend_field="metal",
color=factor_cmap('metal', palette=list(cmap.values()), factors=list(cmap.keys())))
text_props = {"source": df, "text_align": "left", "text_baseline": "middle"}
x = dodge("group", -0.4, range=p.x_range)
p.text(x=x, y="period", text="symbol", text_font_style="bold", **text_props)
p.text(x=x, y=dodge("period", 0.3, range=p.y_range), text="atomic number",
text_font_size="11px", **text_props)
p.text(x=x, y=dodge("period", -0.35, range=p.y_range), text="name",
text_font_size="7px", **text_props)
p.text(x=x, y=dodge("period", -0.2, range=p.y_range), text="atomic mass",
text_font_size="7px", **text_props)
p.text(x=["3", "3"], y=["VI", "VII"], text=["LA", "AC"], text_align="center", text_baseline="middle")
p.outline_line_color = None
p.grid.grid_line_color = None
p.axis.axis_line_color = None
p.axis.major_tick_line_color = None
p.axis.major_label_standoff = 0
p.legend.orientation = "horizontal"
p.legend.location ="top_center"
p.hover.renderers = [r] # only hover element boxes
show(p)
import pandas as pd
from bokeh.models import ColumnDataSource, LabelSet
from bokeh.plotting import figure, output_file, show
from bokeh.sampledata.periodic_table import elements
elements = elements.copy()
elements = elements[elements["atomic number"] <= 82]
elements = elements[~pd.isnull(elements["melting point"])]
mass = [float(x.strip("[]")) for x in elements["atomic mass"]]
elements["atomic mass"] = mass
palette = ["#053061", "#2166ac", "#4393c3", "#92c5de", "#d1e5f0",
"#f7f7f7", "#fddbc7", "#f4a582", "#d6604d", "#b2182b", "#67001f"]
melting_points = elements["melting point"]
low = min(melting_points)
high = max(melting_points)
melting_point_inds = [int(10*(x-low)/(high-low)) for x in melting_points] #gives items in colors a value from 0-10
elements['melting_colors'] = [palette[i] for i in melting_point_inds]
TITLE = "Density vs Atomic Weight of Elements (colored by melting point)"
TOOLS = "hover,pan,wheel_zoom,box_zoom,reset,save"
p = figure(tools=TOOLS, toolbar_location="above", plot_width=1200, title=TITLE)
p.toolbar.logo = "grey"
p.background_fill_color = "#dddddd"
p.xaxis.axis_label = "atomic weight (amu)"
p.yaxis.axis_label = "density (g/cm^3)"
p.grid.grid_line_color = "white"
p.hover.tooltips = [
("name", "@name"),
("symbol:", "@symbol"),
("density", "@density"),
("atomic weight", "@{atomic mass}"),
("melting point", "@{melting point}")
]
source = ColumnDataSource(elements)
p.circle("atomic mass", "density", size=12, source=source,
color='melting_colors', line_color="black", fill_alpha=0.8)
labels = LabelSet(x="atomic mass", y="density", text="symbol", y_offset=8,
text_font_size="11px", text_color="#555555",
source=source, text_align='center')
p.add_layout(labels)
output_file("elements.html", title="elements.py example")
show(p)
from collections import OrderedDict
from io import StringIO
from math import log, sqrt
import numpy as np
import pandas as pd
from bokeh.plotting import figure, output_file, show
antibiotics = """
bacteria, penicillin, streptomycin, neomycin, gram
Mycobacterium tuberculosis, 800, 5, 2, negative
Salmonella schottmuelleri, 10, 0.8, 0.09, negative
Proteus vulgaris, 3, 0.1, 0.1, negative
Klebsiella pneumoniae, 850, 1.2, 1, negative
Brucella abortus, 1, 2, 0.02, negative
Pseudomonas aeruginosa, 850, 2, 0.4, negative
Escherichia coli, 100, 0.4, 0.1, negative
Salmonella (Eberthella) typhosa, 1, 0.4, 0.008, negative
Aerobacter aerogenes, 870, 1, 1.6, negative
Brucella antracis, 0.001, 0.01, 0.007, positive
Streptococcus fecalis, 1, 1, 0.1, positive
Staphylococcus aureus, 0.03, 0.03, 0.001, positive
Staphylococcus albus, 0.007, 0.1, 0.001, positive
Streptococcus hemolyticus, 0.001, 14, 10, positive
Streptococcus viridans, 0.005, 10, 40, positive
Diplococcus pneumoniae, 0.005, 11, 10, positive
"""
drug_color = OrderedDict([
("Penicillin", "#0d3362"),
("Streptomycin", "#c64737"),
("Neomycin", "black" ),
])
gram_color = OrderedDict([
("negative", "#e69584"),
("positive", "#aeaeb8"),
])
df = pd.read_csv(StringIO(antibiotics),
skiprows=1,
skipinitialspace=True,
engine='python')
width = 800
height = 800
inner_radius = 90
outer_radius = 300 - 10
minr = sqrt(log(.001 * 1E4))
maxr = sqrt(log(1000 * 1E4))
a = (outer_radius - inner_radius) / (minr - maxr)
b = inner_radius - a * maxr
def rad(mic):
return a * np.sqrt(np.log(mic * 1E4)) + b
big_angle = 2.0 * np.pi / (len(df) + 1)
small_angle = big_angle / 7
p = figure(plot_width=width, plot_height=height, title="",
x_axis_type=None, y_axis_type=None,
x_range=(-420, 420), y_range=(-420, 420),
min_border=0, outline_line_color="black",
background_fill_color="#f0e1d2")
p.xgrid.grid_line_color = None
p.ygrid.grid_line_color = None
# annular wedges
angles = np.pi/2 - big_angle/2 - df.index.to_series()*big_angle
colors = [gram_color[gram] for gram in df.gram]
p.annular_wedge(
0, 0, inner_radius, outer_radius, -big_angle+angles, angles, color=colors,
)
# small wedges
p.annular_wedge(0, 0, inner_radius, rad(df.penicillin),
-big_angle+angles+5*small_angle, -big_angle+angles+6*small_angle,
color=drug_color['Penicillin'])
p.annular_wedge(0, 0, inner_radius, rad(df.streptomycin),
-big_angle+angles+3*small_angle, -big_angle+angles+4*small_angle,
color=drug_color['Streptomycin'])
p.annular_wedge(0, 0, inner_radius, rad(df.neomycin),
-big_angle+angles+1*small_angle, -big_angle+angles+2*small_angle,
color=drug_color['Neomycin'])
# circular axes and lables
labels = np.power(10.0, np.arange(-3, 4))
radii = a * np.sqrt(np.log(labels * 1E4)) + b
p.circle(0, 0, radius=radii, fill_color=None, line_color="white")
p.text(0, radii[:-1], [str(r) for r in labels[:-1]],
text_font_size="11px", text_align="center", text_baseline="middle")
# radial axes
p.annular_wedge(0, 0, inner_radius-10, outer_radius+10,
-big_angle+angles, -big_angle+angles, color="black")
# bacteria labels
xr = radii[0]*np.cos(np.array(-big_angle/2 + angles))
yr = radii[0]*np.sin(np.array(-big_angle/2 + angles))
label_angle=np.array(-big_angle/2+angles)
label_angle[label_angle < -np.pi/2] += np.pi # easier to read labels on the left side
p.text(xr, yr, df.bacteria, angle=label_angle,
text_font_size="12px", text_align="center", text_baseline="middle")
# OK, these hand drawn legends are pretty clunky, will be improved in future release
p.circle([-40, -40], [-370, -390], color=list(gram_color.values()), radius=5)
p.text([-30, -30], [-370, -390], text=["Gram-" + gr for gr in gram_color.keys()],
text_font_size="9px", text_align="left", text_baseline="middle")
p.rect([-40, -40, -40], [18, 0, -18], width=30, height=13,
color=list(drug_color.values()))
p.text([-15, -15, -15], [18, 0, -18], text=list(drug_color),
text_font_size="12px", text_align="left", text_baseline="middle")
output_file("burtin.html", title="burtin.py example")
show(p)
import numpy as np
import pandas as pd
from bokeh.plotting import figure, output_file, show
# generate some synthetic time series for six different categories
cats = list("abcdef")
yy = np.random.randn(2000)
g = np.random.choice(cats, 2000)
for i, l in enumerate(cats):
yy[g == l] += i // 2
df = pd.DataFrame(dict(score=yy, group=g))
# find the quartiles and IQR for each category
groups = df.groupby('group')
q1 = groups.quantile(q=0.25)
q2 = groups.quantile(q=0.5)
q3 = groups.quantile(q=0.75)
iqr = q3 - q1
upper = q3 + 1.5*iqr
lower = q1 - 1.5*iqr
# find the outliers for each category
def outliers(group):
cat = group.name
return group[(group.score > upper.loc[cat]['score']) | (group.score < lower.loc[cat]['score'])]['score']
out = groups.apply(outliers).dropna()
# prepare outlier data for plotting, we need coordinates for every outlier.
if not out.empty:
outx = []
outy = []
for keys in out.index:
outx.append(keys[0])
outy.append(out.loc[keys[0]].loc[keys[1]])
p = figure(tools="", background_fill_color="#efefef", x_range=cats, toolbar_location=None)
# if no outliers, shrink lengths of stems to be no longer than the minimums or maximums
qmin = groups.quantile(q=0.00)
qmax = groups.quantile(q=1.00)
upper.score = [min([x,y]) for (x,y) in zip(list(qmax.loc[:,'score']),upper.score)]
lower.score = [max([x,y]) for (x,y) in zip(list(qmin.loc[:,'score']),lower.score)]
# stems
p.segment(cats, upper.score, cats, q3.score, line_color="black")
p.segment(cats, lower.score, cats, q1.score, line_color="black")
# boxes
p.vbar(cats, 0.7, q2.score, q3.score, fill_color="#E08E79", line_color="black")
p.vbar(cats, 0.7, q1.score, q2.score, fill_color="#3B8686", line_color="black")
# whiskers (almost-0 height rects simpler than segments)
p.rect(cats, lower.score, 0.2, 0.01, line_color="black")
p.rect(cats, upper.score, 0.2, 0.01, line_color="black")
# outliers
if not out.empty:
p.circle(outx, outy, size=6, color="#F38630", fill_alpha=0.6)
p.xgrid.grid_line_color = None
p.ygrid.grid_line_color = "white"
p.grid.grid_line_width = 2
p.xaxis.major_label_text_font_size="16px"
output_file("boxplot.html", title="boxplot.py example")
show(p)
from bokeh.layouts import row
from bokeh.plotting import figure, output_file, show
factors = ["a", "b", "c", "d", "e", "f", "g", "h"]
x = [50, 40, 65, 10, 25, 37, 80, 60]
dot = figure(title="Categorical Dot Plot", tools="", toolbar_location=None,
y_range=factors, x_range=[0,100])
dot.segment(0, factors, x, factors, line_width=2, line_color="green", )
dot.circle(x, factors, size=15, fill_color="orange", line_color="green", line_width=3, )
factors = ["foo 123", "bar:0.2", "baz-10"]
x = ["foo 123", "foo 123", "foo 123", "bar:0.2", "bar:0.2", "bar:0.2", "baz-10", "baz-10", "baz-10"]
y = ["foo 123", "bar:0.2", "baz-10", "foo 123", "bar:0.2", "baz-10", "foo 123", "bar:0.2", "baz-10"]
colors = [
"#0B486B", "#79BD9A", "#CFF09E",
"#79BD9A", "#0B486B", "#79BD9A",
"#CFF09E", "#79BD9A", "#0B486B"
]
hm = figure(title="Categorical Heatmap", tools="hover", toolbar_location=None,
x_range=factors, y_range=factors)
hm.rect(x, y, color=colors, width=1, height=1)
output_file("categorical.html", title="categorical.py example")
show(row(hm, dot, sizing_mode="scale_width")) # open a browser